Chemometrics - Matthias Otto

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Résumé :
Chemometrics

Explore chemometrics from basic statistics to the latest artificial intelligence and neural network developments in this new edition

Chemometrics is an area of study combining chemistry and mathematics. It governs the interpretation of data generated by chemical analysis, and its growth as a subfield promises to streamline and revolutionize analytical chemistry.

Chemometrics has long been the leading introductory textbook in this subject. Beginning with an introduction to the statistical-mathematical evaluation of chemical measurements, it leads readers through modern chemometric approaches in a pedagogically sound and highly readable style. Now fully updated to reflect the latest research and applications of this exciting discipline, it provides essential tools for a new generation of analytical chemists.

Readers of the fourth edition of Chemometrics will also find:

• New or expanded treatment of subjects such as deep learning, ANNOVA simultaneous component analysis, instrumental data output, and more
• Detailed discussion of approaches to signal processing, design and optimization of experiments, pattern recognition and classification, and many other areas
• Balance of theoretical and practical knowledge to enable rapid application of key techniques

Chemometrics is ideal for advanced students in chemistry, analytical chemistry, pharmaceutical chemistry, biochemistry, or related subjects, and as a useful reference for practicing researchers and laboratory professionals....

Biographie:

Preface vii

List of Abbreviations xi

1 What is Chemometrics? 1

1.1 The Computer-Based Laboratory 3

1.2 Statistics and Data Interpretation 11

1.3 Computer-Based Information Systems/Artificial Intelligence 12

General Reading 13

Questions and Problems 13

2 Basic Statistics 15

2.1 Descriptive Statistics 16

2.2 Statistical Tests 28

2.3 Analysis of Variance 45

General Reading 56

Questions and Problems 57

3 Signal Processing and Time Series Analysis 61

3.1 Signal Processing 62

3.2 Time Series Analysis 91

General Reading 99

Questions and Problems 100

4 Optimization and Experimental Design 101

4.1 Systematic Optimization 102

4.2 Objective Functions and Factors 103

4.3 Experimental Design and Response Surface Methods 111

4.4 Sequential Optimization: Simplex Method 135

General Reading 142

Questions and Problems 142

5 Pattern Recognition and Classification 145

5.1 Preprocessing of Data 147

5.2 Unsupervised Methods 151

5.3 Supervised Methods 198

General Reading 226

Questions and Problems 227

6 Modeling 231

6.1 Univariate Linear Regression 232

6.2 Multiple Linear Regression 249

6.3 Nonlinear Methods 281

General Reading 293

Questions and Problems 293

7 Analytical Databases 295

7.1 Representation of Analytical Information 296

7.2 Library Search 309

7.3 Simulation of Spectra 316

General Reading 318

Questions and Problems 318

8 Knowledge Processing and Soft Computing 321

8.1 Artificial Intelligence and Expert Systems 321

8.2 Neural Networks 330

8.3 Fuzzy Theory 352

8.4 Genetic Algorithms and Other Global Search Strategies 365

General Reading 375

Questions and Problems 377

9 Quality Assurance and Good Laboratory Practice 379

9.1 Validation and Quality Control 380

9.2 Accreditation and Good Laboratory Practice 384

General Reading 386

Questions and Problems 386

Appendix 387

Index 403